Abstract
While syntactic dependency annotations concentrate on the surface or functional structure of a sentence, semantic dependency annotations aim to capture between-word relationships that are more closely related to the meaning of a sentence, using graph-structured representations. We extend the LSTM-based syntactic parser of Dozat and Manning (2017) to train on and generate these graph structures. The resulting system on its own achieves state-of-the-art performance, beating the previous, substantially more complex state-of-the-art system by 0.6% labeled F1. Adding linguistically richer input representations pushes the margin even higher, allowing us to beat it by 1.9% labeled F1.- Anthology ID:
- P18-2077
- Volume:
- Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
- Month:
- July
- Year:
- 2018
- Address:
- Melbourne, Australia
- Editors:
- Iryna Gurevych, Yusuke Miyao
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 484–490
- Language:
- URL:
- https://aclanthology.org/P18-2077
- DOI:
- 10.18653/v1/P18-2077
- Cite (ACL):
- Timothy Dozat and Christopher D. Manning. 2018. Simpler but More Accurate Semantic Dependency Parsing. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 484–490, Melbourne, Australia. Association for Computational Linguistics.
- Cite (Informal):
- Simpler but More Accurate Semantic Dependency Parsing (Dozat & Manning, ACL 2018)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-3/P18-2077.pdf
- Code
- additional community code
- Data
- Universal Dependencies